Traditionally, medical imaging has focused on providing an accurate geometric description of the anatomical structures being imaged. Recent studies have highlighted the power of medical images towards obtaining functional quantification of organ behavior. Some notable examples are the ability to non-invasively compute blood pressures from images, ability to compute strain and stress in biological tissues and models to detect irregularities in electrical signal propagation in cardiac arrhythmias. These models combine the anatomic information obtained from medical images with models derived from physics to provide significant insight into the patient's pathology.
A drawback of the aforementioned conventional techniques is that a detailed organ segmentation must be used as an input to compute the relevant biomarkers. Typically, a segmentation operation is performed as an intermediary step between imaging and modeling. During this operation, a segmented mesh is created based on the imaging data. Then, this mesh is used as input into the computational models. The segmentation operation is frequently the source of much uncertainty and inter-user variability. Further, it is typically an effort-intensive process requiring much attention. For example, the time required to perform the segmentation operation is often equal to, if not greater than, the time required to execute the computational model. Accordingly, it is desired to create a technique for deriving biomarkers without prior segmentation.